AAMI News September 2017

Clinicians Monitor the Present to Predict the Future

For some, the concept of “continuous monitoring” means collecting information about patients in real time to see if their condition requires immediate medical intervention. But for others, continuous monitoring is more than a peephole view into the present. Rather, it’s a telescope that looks over the horizon to help see a patient’s future—and make course corrections along the way.

In a roundtable discussion published in the July/August 2017 issue of BI&T, R. Scott Evans, medical informatics director at Intermountain Healthcare in Salt Lake City, UT, described his hospital’s initiative to predict and intervene in cases of patient deterioration—the Early Predictor of Deterioration (ePOD).

“We heard from our medical emergency team (MET) that many patients were not being identified as soon as possible and sometimes could have been identified up to 12 hours earlier,” said Evans. “We tried to figure out if we could use clinical decision support to identify those patients and alert nursing as soon as possible.”

ePOD collects vital signs and other relevant health information, such as heart rate, blood pressure, respiratory rate, and neurological scores, from physiologic monitors and the electronic health records of acute care patients at regular intervals. A computer algorithm developed to predict physiologic deterioration then analyzes this data and assigns a score for the patient. If the score is too high, nursing staff is alerted using a pager system and a secure website that displays the deterioration signs graphically.

The project, which Evans and others at Intermountain described in a 2014 study published in the Journal of the American Medical Informatics Association, resulted in more appropriate calls to the MET and fewer patient deaths.

J. Randall Moorman, a professor of internal medicine, physiology, and biomedical engineering at the University of Virginia, described another life-saving initiative during his keynote address at the AAMI 2017 Conference & Expo last June. Moorman and others have collected more than 100 terabytes of data from continuous electronic monitors to develop a “risk estimation device” called CoMET (continuous monitoring of event trajectories).

CoMET capitalizes on an abnormality in heat rate patterns that is observed in premature infants who are likely to develop sepsis. A predictive model uses this information to help predict sepsis—and allow for treatment to begin—before clinical signs appear. According to Moorman, that prediction saves one life per 48 very low birthweight infants (less than 1,500 g) and one life per 23 extremely low birthweight infants (less than 1,000 g).

Moorman and his team are now working to use similar predictive methods to reduce deaths in adults.